code: def generate_model_output_with_no_positive_examples() -> (
      Dict[str, torch._tensor.Tensor]
  ):
      return {
          "predictions": torch.tensor(
              [
                  [
                      1.0,
                      0.0,
                      0.51,
                      0.8,
                      1.0,
                      0.0,
                      0.51,
                      0.8,
                      1.0,
                      0.0,
                      0.51,
                      0.8,
                  ]
              ]
          ),
          "session": torch.tensor(
              [[1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1]]
          ),
          "labels": torch.tensor([[0.0] * 12]),
          "weights": torch.tensor(
              [
                  [
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                  ]
              ]
          ),
          "expected_recall": torch.tensor([float("nan")]),
      }
  
file_path: torchrec/torchrec/metrics/tests/test_recall_session.py
imports: import unittest
  from typing import Dict, Optional, Union
  import torch
  from torch import no_grad
  from torchrec.metrics.metrics_config import RecTaskInfo, SessionMetricDef
  from torchrec.metrics.rec_metric import RecMetricException
  from torchrec.metrics.recall_session import RecallSessionMetric
label_desc: Use image node_id 3 for calling a global function with example usage: generate_model_output_with_no_positive_examples() and returns: dict